VOLLEYBALL
PLAYERS AND
ACTIONS DETECTION
OBJECT DETECTION USING
YOLO FRAMEWORK
GUIDE : Dr. SESHAM ANAND
2451-20-733-001 A SAI SANTHOSH
2451-20-733-003 TANDRA AKASH SHARMA
2451-20-733-039 YASHWANTH PONUGOTI
Presentation Overview 0 2
KEY TOPICS
Problem Statement
Scope of the Project
Development Environment
Literature Survey
Hardware & Software Requirements
Model Structure
Project Workflow
Object Detection Evaluation
Problem Statement 03
Develop an efficient and robust system for
real-time volleyball player and action
detection using the YOLO (You Only Look
Once) object detection model. The objective
is to enhance the sports analysis experience
by automatically identifying and tracking
players, as well as recognizing various
volleyball actions during a match.
Scope of the Project 04
This project involves developing a real-time volleyball analysis
system using YOLO-based object detection. The scope includes
creating specialized models for accurate player detection and
action recognition, optimizing for low-latency processing. The
system will be designed to handle varying player poses,
occlusions, and distinguish between teams. Emphasis will be placed
on robustness against lighting and uniform variations. A user-
friendly interface will visualize player movements and actions,
with scalability for different court configurations. The project
encompasses comprehensive documentation, testing, and integration,
aiming to contribute a cutting-edge solution for enhanced sports
analytics and an enriched viewer experience in volleyball matches.
P L A Y E R
DETECTION
M O D U L E
SCALABILITY AND
ADAPTABILITY
TESTING AND
VALIDATION
REAL-TIME
PROCESSING
OPTIMIZATION
REPORTING AND
ANALYTICS
PERFORMANCE
M E T R I C S
INTEGRATION
WITH EXISTING
S Y S T E M S
A C C U R A C Y
ENHANCEMENT AND
ROBUSTNESS
A C T I O N
RECOGNITION
M O D U L E
Key Features/GoalS
4.1
USER INTERFACE
DEVELOPMENT
DEVELOPMENT
ENVIRONMENT
INTEGRATED DEVELOPMENT
ENVIRONMENT(IDE):
Jupyter Notebook
Visual Studio Code
PROGRAMMING LANGUAGES:
Pyhton
Libraries:
ultralytics
openCV
pytorch
HTML,CSS
05
Project Pilot
Literature Survey 0 6
Object Detection and Tracking Using Yolo
Object Detection and Tracking Using Yolo
LINK TO PAPER
Abstract : Artificial Intelligence is being adapted by the world since
past few years and deep learning played a crucial role in it. This paper
focuses on deep learning and how it is applied to detect and track the
objects.Detection and tracking algorithms are specified in terms of
extricating the features of images and videos for security and scrutiny
applications. Popular algorithms of object detection include You Only
Look Once (YOLO), Region-based Convolutional Neural Networks (RCNN),
Faster RCNN (F-RCNN). RCNN has better accuracy compared to other
algorithms but YOLO surpasses when speed is considered over accuracy. In
YOLO, Object detection is implemented as a regression problem and class
probabilities are provided for detected images.
Literature Survey 0 7
Analytical Study on Object Detection using Yolo
Analytical Study on Object Detection using Yolo
Algorithm
Algorithm
LINK TO PAPER
Abstract : Object detection is a technique that allows detecting and
locating objects in videos and images. Object detection is widely used
to count objects in a scene, track their precise locations and
accurately label the objects. It seeks to answer what is the object?
and Where is it? . Object detection adopts various approaches such as
fast R-CNN, Retina-Net, Single Shot MultiBox Detector (SSD) and YOLO.
Among these, YOLO is the most powerful algorithm for object detection
and as well as suited for real-time scenarios. It is popular because
of its accuracy and speed. YOLO uses Neural networks to provide object
detection.
Literature Survey 0 8
Object Detection in
Object Detection in
Sports Videos
Sports Videos
LINK TO PAPER
Abstract : Object detection is commonly used in many computer vision
applications. In our case, we need to apply the object detector as a
prerequisite for action recognition in handball scenes. Object detection, to
be successful for this task, should be as accurate as possible and should be
able to deal with a different number of objects of various sizes, partially
occluded, with bad illumination and deal with cluttered scenes. The aim of
this paper is to provide an overview of the current state-of-the-art
detection methods that rely on convolutional neural networks (CNNs) and test
their performance on custom video sports materials acquired during handball
training and matches. The comparison of the detector performance in
different conditions will be given and discussed.
Tools And Technologies
Laptop with 8GB RAM,
macOS
Storage of minimum
50GB is required
Internet Connectivity
Hardware Req. Software Req.
Operating System (OS):
Windows 10, Windows 11, or
macOS.
Integrated Development
Environment(IDE):
Jupyter notebook
09
Model structure of YOLOv8
detection models(P5) -
yolov8n/s/m/l/x
10
Workflow of
the project
11
The IoU is calculated by taking the intersection area of two bounding
boxes and dividing it by the union area of these boxes. In mathematical
terms, the IoU formula is expressed as follows:
This formula essentially quantifies the degree of overlap between the
predicted bounding box and the ground truth bounding box. It provides a
numerical value that indicates how well the model’s prediction aligns
with the actual object location. A higher IoU score indicates a better
match between the predicted and ground truth bounding boxes, signifying
superior localization accuracy.
12 Object Detection Evaluation
Intersection over Union (IoU):Intersection over Union (IoU), also known
as Jaccard’s Index, serves as a pivotal metric in the realm of computer
vision, particularly for tasks like object detection and segmentation.
13 Object Detection Evaluation
Precision, Recall and F1-Score
Precision, Recall, and F1-Score are fundamental metrics used to assess the
performance of object detection models. These metrics provide valuable
insights into the model’s ability to identify objects of interest within
images. Before delving into these metrics, let’s establish some fundamental
concepts
14 Object Detection Evaluation
Precision, Recall and F1-Score
15 Mean Average Precision(mAP)
Mean Average Precision (mAP) is a performance metric commonly used
in object detection tasks, including those involving YOLO models.
It assesses the precision and recall of a model across multiple
classes.
In object detection, precision measures the accuracy of positive
predictions, while recall gauges the ability to capture all
relevant instances. mAP is the average precision calculated for
each class, and the mean is taken over all classes. The higher the
mAP, the better the model's overall performance in accurately
identifying and localizing objects. It provides a consolidated
measure that considers both precision and recall, offering a more
comprehensive evaluation of an object detection model.
16 Mean Average Precision(mAP)
THANK YOU